leaflet() initialises a leaflet object m. Pipe this into addCircles() where we refer to the map_data dataframe and indicate which columns correspond to latitude and longitude.

m <- leaflet() %>%
  addCircles(data = map_data, lng = ~x, lat = ~y)
m 

Tile give our data geographic context. addTiles() uses OpenStreetMap tiles, the default for leaflet

m %>% addTiles()

Allows us to use third party tiles (e.g. Terrain tiles from Stamen Design).

# m created using code from previous slides
m <- leaflet() %>%
  addCircles(data=map_data, lng=~x, lat=~y)

Code options

addProviderTiles(map = m, provider = "Stamen.Terrain")

addProviderTiles(map = m, provider = providers$Stamen.Terrain)

m %>% addProviderTiles(provider = providers$Stamen.Terrain)

m %>% addProviderTiles(providers$Stamen.Terrain)

m %>% addProviderTiles(providers$Esri.NatGeoWorldMap)

m %>% addProviderTiles(providers$Wikimedia)

m %>% addProviderTiles(providers$Esri.WorldTopoMap)

m %>% addProviderTiles(providers$Esri.WorldImagery)

m %>% addProviderTiles(providers$CartoDB.Positron)

Circle markers (addCircleMarkers()) are similar to circles but radius in onscreen pixels is constant regardless of zoom level.

m %>% addCircleMarkers(data=map_data, lng=~x, lat=~y, popup = popupInfo, radius = 5,
                       stroke = T, weight = 3, color = "grey", opacity = 0.8,
                       fillColor = ifelse(map_data$species == "human",
                                          "firebrick", "dodgerblue"), fillOpacity = 0.7)

With, stroke = F, weight, color, and opacity are irrelevant. Can also use radius = to size circle markers by species.

m %>% addCircleMarkers(data = map_data, lng = ~x, lat = ~y, popup = popupInfo, stroke = F,
                       radius = ifelse(map_data$species == "human", 8, 4),
                       fillColor = ifelse(map_data$species == "human", "firebrick", "dodgerblue"),
                       fillOpacity = 0.8)

On a map with many markers, it might make sense to cluster them using clusterOptions = markerClusterOptions() shown here with the default options for markerClusterOptions().

m %>% addMarkers(data = map_data, lng = ~x, lat = ~y, label = ~species, popup = popupInfo)

On a map with many markers, it might make sense to cluster them using clusterOptions = markerClusterOptions() shown here with the default options for markerClusterOptions().

m %>% addMarkers(data = map_data, lng = ~x, lat = ~y, label = ~species, popup = popupInfo,
                 clusterOptions = markerClusterOptions())

leaflet::colourFactor() creates a function (we're going to call it myPal) that is used as an argument when we use addCircles(), addCircleMarkers()etc.

# Choose 3 colours for our 3 groups  
colour_pal <- c("gold", "forestgreen", "cornflowerblue")
myPal <- colorFactor(palette = colour_pal,
                     domain = c("Human", "Domestic", "Wildlife"))

Create a fresh version of m before we add coloured circles

m <- leaflet() %>% 
  addProviderTiles(provider = providers$CartoDB.Positron)

myPal() takes species type variable as argument

m <- m %>% addCircles(data=map_data, lng=~x, lat=~y, 
                      color = myPal(map_data$species_type),
                      opacity = 0.9, popup = popupInfo)
m

Adapt the code from previous slides to produce a map with:
1) tiles showing topography
2) circles coloured by species (instead of species type)
3) a colour legend
4) a scale bar showing distance in kilometers (hint: ?addScaleBar).

Previous code:

colour_pal <- c("gold", "forestgreen", "cornflowerblue")
myPal <- colorFactor(colour_pal, domain = c("Human", "Domestic", "Wildlife"))
m <- leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.Positron) %>%
  addCircles(data = map_data, lng = ~x, lat = ~y,
             color = myPal(map_data$species_type), opacity = 0.9) %>%
  addLegend(position = "topright", title = "Species<br>type", pal = myPal,
            values = map_data$species_type, opacity = 0.9)
m

Part 1) tiles showing topography

m <- leaflet() %>%
  addProviderTiles(provider = providers$OpenTopoMap)
m

Part 2) circles coloured by species (instead of species type)

colour_pal <- c("gold", "forestgreen", "cornflowerblue", "red", "magenta")
myPal <- colorFactor(colour_pal, domain = unique(map_data$species))
m <- m %>% addCircles(data = map_data, lng = ~x, lat = ~y,
                      color = myPal(map_data$species), opacity = 0.9)
m

Part 2) circles coloured by species (instead of species type)

myPal <- colorFactor("Set2", domain = unique(map_data$species))
m <- m %>% addCircles(data = map_data, lng = ~x, lat = ~y,
                      color = myPal(map_data$species), opacity = 0.9)
m

Part 3) a colour legend

m <- m %>% addLegend(position = "topright", title = "Species", pal = myPal,
                     values = map_data$species, opacity = 0.9)
m

Part 4) a scale bar showing distance in kilometers

m <- m %>% addScaleBar(position = "bottomleft",
                       options = scaleBarOptions(maxWidth = 200, # default = 100
                                                 imperial = FALSE))
m

Add circles and a legend referring to myPal

m %>% addCircles(data = map_data, lng = ~x, lat = ~y,
                 color = myPal(map_data$date_decimal), opacity = 0.9) %>% 
  addLegend(position = "topright", title = "Date",
            pal = myPal, values = dateRange, opacity = 0.9,
            labFormat = labelFormat(big.mark = "")) # removes a comma from 2,014

Adapt the code from previous slides to produce a map with:
1) Circles coloured by population density using the viridis palette
2) and sized by population density (higher density = larger circles)
3) legend and scale bar

Previous code to adapt:

dateRange <- c(2014, 2015)
myPal <- colorNumeric(palette = "Spectral", domain = dateRange)
m <- leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.Positron) %>%
  addCircles(data = map_data, lng = ~x, lat = ~y,
             color = myPal(map_data$date_decimal), opacity = 0.9) %>% 
  addLegend(position = "topright", title = "Date",
            pal = myPal, values = dateRange, opacity = 0.9,
            labFormat = labelFormat(big.mark = ""))
m

myPal <- colorQuantile(palette = "viridis", domain = map_data$density)

leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.Positron) %>%
  addCircles(data = map_data, lng = ~x, lat = ~y, radius = 10,
             color = myPal(map_data$density), opacity = 0.9) %>% 
  addLegend(position = "topright", title = "Human<br>density",
            pal = myPal, values = map_data$density, opacity = 0.9,
            labFormat = labelFormat(big.mark = ""))

Load shape data and look at the structure

region_shp <- rgdal::readOGR(dsn = "data/TZ_Region_2012_density")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/wharvey/Documents/git/ShinyCourse/Day 2/slides/data/TZ_Region_2012_density", layer: "TZ_Region_2012.density"
## with 30 features
## It has 4 fields

head(region_shp)

## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
##      Region_Nam               Loc_ID Loc_type    density
## 0         Tanga         Region-Tanga   Region  150.13810
## 1        Tabora        Region-Tabora   Region   94.02598
## 2  Kusini Pemba  Region-Kusini Pemba   Region  770.20632
## 3          Mara          Region-Mara   Region 1002.31128
## 4       Manyara       Region-Manyara   Region  388.34348
## 5 Kusini Unguja Region-Kusini Unguja   Region  259.94865
## 
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 38.276477 -5.217057
## 
## Slot "area":
## [1] 2.275229
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##            [,1]      [,2]
##   [1,] 38.79584 -5.962273
##   [2,] 38.79085 -5.966766
##   [3,] 38.75925 -5.953226
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##  [66,] 38.10005 -5.861002
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##  [70,] 38.03036 -5.858953
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##  [72,] 38.01474 -5.858953
##  [73,] 38.00679 -5.860234
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##  [75,] 37.99783 -5.872789
##  [76,] 37.98963 -5.858696
##  [77,] 37.97041 -5.848191
##  [78,] 37.96017 -5.851010
##  [79,] 37.94736 -5.848960
##  [80,] 37.94428 -5.843066
##  [81,] 37.92609 -5.842810
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##  [84,] 37.89906 -5.872063
##  [85,] 37.88882 -5.871721
##  [86,] 37.87993 -5.876162
##  [87,] 37.86525 -5.891877
##  [88,] 37.84987 -5.888461
##  [89,] 37.83723 -5.888119
##  [90,] 37.81913 -5.884020
##  [91,] 37.79975 -5.891914
##  [92,] 37.79292 -5.890657
##  [93,] 37.78794 -5.895956
##  [94,] 37.77668 -5.893480
##  [95,] 37.77028 -5.886653
##  [96,] 37.75443 -5.888554
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## [156,] 37.14768 -5.990333
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## [183,] 37.20541 -5.714694
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## [185,] 37.16526 -5.716034
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## [188,] 37.21825 -5.649763
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## [280,] 38.42793 -4.125216
## [281,] 38.49105 -4.168957
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## [293,] 39.18725 -4.716455
## [294,] 39.17839 -4.726915
## [295,] 39.17025 -4.732799
## [296,] 39.16675 -4.743913
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## [298,] 39.17546 -4.763356
## [299,] 39.16774 -4.775623
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## [301,] 39.17663 -4.795533
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## [303,] 39.18955 -4.805756
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## [305,] 39.20920 -4.799812
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## [308,] 39.22006 -4.806627
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## [312,] 39.19879 -4.850702
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## [315,] 39.21417 -4.843052
## [316,] 39.21131 -4.857405
## [317,] 39.20445 -4.873095
## [318,] 39.19727 -4.880539
## [319,] 39.19322 -4.894134
## [320,] 39.18546 -4.906348
## [321,] 39.18336 -4.920255
## [322,] 39.17360 -4.933494
## [323,] 39.17837 -4.910360
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## [325,] 39.18234 -4.888161
## [326,] 39.17757 -4.882946
## [327,] 39.17779 -4.868549
## [328,] 39.17441 -4.866052
## [329,] 39.17803 -4.852473
## [330,] 39.17143 -4.844015
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## [629,] 35.53548 -4.742217
## [630,] 35.52726 -4.762154
## [631,] 35.51735 -4.778879
## [632,] 35.51180 -4.792152
## [633,] 35.50592 -4.816895
## [634,] 35.48724 -4.839502
## [635,] 35.47967 -4.844263
## [636,] 35.45983 -4.847193
## [637,] 35.41589 -4.867188
## [638,] 35.40253 -4.859680
## [639,] 35.37610 -4.864319
## [640,] 35.35870 -4.856506
## [641,] 35.35010 -4.856079
## [642,] 35.34037 -4.876764
## 
## 
## 
## Slot "plotOrder":
## [1] 1
## 
## Slot "labpt":
## [1] 36.54808 -4.53372
## 
## Slot "ID":
## [1] "4"
## 
## Slot "area":
## [1] 3.78614
## 
## 
## [[6]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 39.423146 -6.238686
## 
## Slot "area":
## [1] 0.07245558
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##            [,1]      [,2]
##   [1,] 39.27878 -6.027963
##   [2,] 39.29502 -6.026602
##   [3,] 39.32966 -6.034940
##   [4,] 39.33859 -6.032004
##   [5,] 39.39572 -6.023934
##   [6,] 39.40121 -6.021609
##   [7,] 39.40990 -6.039812
##   [8,] 39.40838 -6.050178
##   [9,] 39.41437 -6.057343
##  [10,] 39.42333 -6.057808
##  [11,] 39.43129 -6.072347
##  [12,] 39.42584 -6.089948
##  [13,] 39.42870 -6.112995
##  [14,] 39.42546 -6.118239
##  [15,] 39.42545 -6.135899
##  [16,] 39.42861 -6.144457
##  [17,] 39.43494 -6.150328
##  [18,] 39.43799 -6.159055
##  [19,] 39.43801 -6.171122
##  [20,] 39.41981 -6.181386
##  [21,] 39.42342 -6.189269
##  [22,] 39.44435 -6.179159
##  [23,] 39.45054 -6.191441
##  [24,] 39.46148 -6.191527
##  [25,] 39.46516 -6.182103
##  [26,] 39.47961 -6.182016
##  [27,] 39.48099 -6.194641
##  [28,] 39.48984 -6.195429
##  [29,] 39.49487 -6.183844
##  [30,] 39.50340 -6.191671
##  [31,] 39.50664 -6.177672
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##  [34,] 39.48969 -6.140820
##  [35,] 39.49637 -6.122608
##  [36,] 39.51079 -6.127606
##  [37,] 39.51917 -6.153461
##  [38,] 39.52822 -6.170188
##  [39,] 39.53477 -6.187206
##  [40,] 39.53575 -6.196637
##  [41,] 39.53338 -6.227418
##  [42,] 39.53649 -6.259255
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##  [44,] 39.54477 -6.310405
##  [45,] 39.55723 -6.342903
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##  [51,] 39.56822 -6.430035
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##  [56,] 39.48760 -6.473494
##  [57,] 39.47371 -6.455829
##  [58,] 39.46571 -6.449421
##  [59,] 39.45634 -6.429154
##  [60,] 39.45565 -6.419205
##  [61,] 39.44184 -6.401707
##  [62,] 39.43411 -6.376402
##  [63,] 39.43319 -6.351902
##  [64,] 39.43078 -6.343436
##  [65,] 39.42136 -6.327594
##  [66,] 39.41158 -6.319457
##  [67,] 39.41494 -6.308694
##  [68,] 39.40787 -6.305172
##  [69,] 39.40435 -6.316413
##  [70,] 39.38646 -6.309847
##  [71,] 39.40099 -6.326545
##  [72,] 39.41546 -6.347157
##  [73,] 39.41606 -6.367958
##  [74,] 39.40995 -6.375005
##  [75,] 39.40301 -6.377186
##  [76,] 39.39851 -6.365993
##  [77,] 39.38734 -6.365165
##  [78,] 39.38579 -6.358258
##  [79,] 39.38918 -6.348141
##  [80,] 39.38337 -6.338810
##  [81,] 39.38306 -6.328763
##  [82,] 39.37382 -6.324133
##  [83,] 39.36123 -6.305649
##  [84,] 39.34744 -6.302858
##  [85,] 39.34127 -6.288876
##  [86,] 39.33780 -6.271705
##  [87,] 39.32724 -6.252795
##  [88,] 39.31797 -6.248465
##  [89,] 39.30996 -6.237057
##  [90,] 39.30686 -6.222955
##  [91,] 39.28778 -6.207119
##  [92,] 39.28505 -6.192836
##  [93,] 39.27457 -6.180707
##  [94,] 39.27866 -6.169825
##  [95,] 39.27063 -6.152037
##  [96,] 39.27192 -6.139334
##  [97,] 39.27000 -6.129695
##  [98,] 39.27406 -6.120969
##  [99,] 39.27811 -6.093694
## [100,] 39.27503 -6.086431
## [101,] 39.27642 -6.070410
## [102,] 39.28593 -6.047105
## [103,] 39.27878 -6.027963
## 
## 
## 
## Slot "plotOrder":
## [1] 1
## 
## Slot "labpt":
## [1] 39.423146 -6.238686
## 
## Slot "ID":
## [1] "5"
## 
## Slot "area":
## [1] 0.07245558
## 
## 
## 
## Slot "plotOrder":
## [1] 2 5 1 4 6 3
## 
## Slot "bbox":
##         min       max
## x 31.038017 39.855694
## y -6.976504 -1.027046
## 
## Slot "proj4string":
## CRS arguments: +proj=longlat +ellps=WGS84 +no_defs

Use addPolygons() to add to leaflet map. label = determines what will be shown when cursor hovers above area.

m <- leaflet() %>% addProviderTiles(provider = providers$Esri.WorldShadedRelief)
m %>% addPolygons(data = region_shp, label = region_shp$Region_Nam,
                  color = "black", weight = 1, fillOpacity = 0.3, opacity = 0.6)

Use highlightOptions() to highlight shapes when cursor hovers above. bringToFront = TRUE avoids potential problems with order in which shapes are coded in shape file.

m %>% addPolygons(data = region_shp, label = region_shp$Region_Nam, fillColor = "gold",
                  color = "black", weight = 1, fillOpacity = 0.3, opacity = 0.6,
                  highlightOptions = highlightOptions(color = "white", weight = 3,
                                                      bringToFront = TRUE, opacity = 1))

Possible to use addPolylines() to add outlines only to leaflet map.

m %>% addPolylines(data = region_shp, color = "red",  weight = 2, opacity = 1)

Choropleth: Map with areas shaded according to some numeric variable
(similar to heatmap but uses geographic boundaries)

myPal <- colorNumeric(palette = "YlOrRd", domain = region_shp$density)
m %>% addPolygons(data = region_shp, label = region_shp$Region_Nam,
                  fillColor = myPal(region_shp$density), fillOpacity = 0.7,
                  color = "white", opacity = 1, weight = 2)

Like before, we can log density to give a more informative map

myPal <- colorNumeric(palette = "YlOrRd", domain = log(region_shp$density))
m %>% addPolygons(data = region_shp, label = region_shp$Region_Nam,
                  fillColor = myPal(log(region_shp$density)), fillOpacity = 0.7,
                  color = "white", opacity = 1, weight = 2)

human_data <- map_data[map_data$species == "human",]
myPal <- colorQuantile(palette = "YlOrRd", domain = region_shp$density)
myPal2 <- colorNumeric(palette = "viridis", domain = human_data$date_decimal)
m %>% addPolygons(data = region_shp, label = region_shp$density,
                  fillColor = myPal(region_shp$density), fillOpacity = 0.7,
                  color = "white", opacity = 1, weight = 2) %>% 
  addCircleMarkers(data = human_data, lat = ~y, lng = ~x,
                   fillColor = myPal2(human_data$date_decimal), fillOpacity = 0.7, radius = 4,
                   stroke = T, weight = 2, color = "black", opacity = 0.8)

labels <- sprintf("<strong>%s</strong><br/>%g people / km<sup>2</sup>",
                   region_shp$Region_Nam, round(region_shp$density, 0)) %>%
  lapply(htmltools::HTML)

m %>% addPolygons(data = region_shp, fillColor = myPal(region_shp$density), label = labels,
                  weight = 2, opacity = 1, color = "white", fillOpacity = 0.7,
                  highlightOptions = highlightOptions(color = "black", weight = 3,
                                                      bringToFront = TRUE, opacity = 1))

Let's log density as we did before

raster::values(density) %>% range(na.rm = T) %>% log

## [1]     -Inf 10.27813

myPal <- colorNumeric(palette = colour_pal, domain = c(-1.5, 11),
                      na.color = "transparent")

We have to exclude some data, as there are 0 density polygons and log(0) gives -infinity

sum(raster::values(density) > exp(-1.5), na.rm = T) /
  sum(raster::values(density) >= 0, na.rm = T)

## [1] 0.9975207

Plot the logged raster data with some tiles and a legend for the colour scheme.

m <- leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.Positron) %>%
  addRasterImage(x = log(density), colors = myPal, opacity = 0.8) %>%
  addLegend(pal = myPal, values = c(-1.5, 11), opacity = 0.8,
            title = "Human<br>population<br>density")

## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA

Instead of using addProviderTiles(provider = providers$CartoDB.Positron)

We order things here so that we plot

1. The background tiles only (providers$CartoDB.PositronNoLabels)
2. Then the raster data (addRasterImage())
3. and finally the labels (providers$CartoDB.PositronOnlyLabels)

m <- leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.PositronNoLabels) %>%
  addRasterImage(x = log(density), colors = myPal, opacity = 0.8) %>%
  addProviderTiles(provider = providers$CartoDB.PositronOnlyLabels) %>%
  addLegend(pal = myPal, values = c(-1.5, 11), opacity = 0.8,
            title = "Human<br>population<br>density")

m <- leaflet() %>%
  addProviderTiles(provider = providers$CartoDB.PositronNoLabels) %>%
  addRasterImage(x = log(density), colors = myPal, opacity = 0.8) %>%
  addProviderTiles(provider = providers$CartoDB.PositronOnlyLabels) %>%
  addLegend(pal = myPal, values = c(-1.5, 11), opacity = 0.8,
            title = "Human<br>population<br>density") %>% 
  addPolygons(data = region_shp, label = region_shp$Region_Nam, fillOpacity = 0,
              color = "black", weight = 1, opacity = 0.6,
              highlightOptions = highlightOptions(color = "white", weight = 3,
                                                  bringToFront = TRUE, opacity = 1))